Titanium Carbide (Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>) MXene for Sequestration of Aquatic Pollutants
Bibliographic record
Abstract
Abstract The rapid expansion of industrialization has resulted in the release of multiple ecological contaminants in gaseous, liquid, and solid forms, which pose significant environmental risks to many different ecosystems. The efficient and cost‐effective removal of these environmental pollutants has attracted global attention. This growing concern has prompted the synthesis and optimization of nanomaterials and their application as potential pollutant removal. In this context, MXene is considered an outstanding photocatalytic candidate due to its unique physicochemical and mechanical properties, which include high specific surface area, physiological compatibility, and robust electrodynamics. This review highlights recent advances in shaping titanium carbide (Ti 3 C 2 T x ) MXenes, emphasizing the importance of termination groups to boost photoactivity and product selectivity, with a primary focus on engineering aspects. First, a broad overview of Ti 3 C 2 T x MXene is provided, delving into its catalytic properties and the formation of surface termination groups to establish a comprehensive understanding of its fundamental catalytic structure. Subsequently, the effects of engineering the morphology of Ti 3 C 2 T x MXene into different structures, such as two‐dimensional (2D) accordion‐like forms, monolayers, hierarchies, quantum dots, and nanotubes. Finally, a concise overview of the removal of different environmental pollutants is presented, and the forthcoming challenges, along with their prospective outlooks, are delineated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".